A novel paradigm in paracrine signaling has recently emerged based on the findings identifying extracellular vesicles (EVs) as intercellular conveyors of biological information both in normal and pathological conditions such as cancer. EVs and their cargo have been shown by us and others to regulate gene expression and alter cell function in various cell types. Moreover, during pathological conditions such as cancer, the number and compositions of EVs alter the host immune response as well as synchronize the behavior of secondary tumors. Isolation and molecular profiling of EVs (i.e. RNAs, proteins, post-translational modifications, lipids, metabolites) both in health and disease are critical for understanding EVs' biogenesis and effector functions. Currently, the study of EVs as biological entities relevant for intracellular signaling and disease diagnosis is based on the assumption that the biogenesis and removal of EVs happen at a steady state rate, being modified mostly by the healthy/diseased status of the host. Our published results show that that is not the case. Our data indicate that the tissue-origin, number, size distribution, as well as protein, lipid, metabolite and RNA composition of EVs isolated by standard techniques depend not just on the blood collection methods, but also on the time of the day the blood samples were collected. Therefore, systematic assessment of these factors and other sources of variability in EV profiles are important for enabling basic biology, clinical and personalized medicine applications. Although it is beyond the purpose of this grant, the long-term goal of our team is to establish reproducible methods for blood collection and sample processing that would allow us to identify the specific molecular EV signatures during the day/night cycle, with the aim of pinpointing the ideal, organ-specific times for blood collection, which would increase the reliability and specificity of early cancer detection. We believe that establishing biological and methodological baselines are absolutely vital for correctly comparing proteomics and glycomics data obtained in various studies, as well as interpreting the data obtained from patients' samples.